26 datasets found
  1. F

    CBOE Crude Oil ETF Volatility Index

    • fred.stlouisfed.org
    json
    Updated Jul 11, 2025
    + more versions
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    (2025). CBOE Crude Oil ETF Volatility Index [Dataset]. https://fred.stlouisfed.org/series/OVXCLS
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 11, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

    Description

    Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-07-10 about ETF, VIX, volatility, crude, oil, stock market, and USA.

  2. f

    Summary statistics for the VIX and VKOSPI.

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Summary statistics for the VIX and VKOSPI. [Dataset]. https://plos.figshare.com/articles/dataset/Summary_statistics_for_the_VIX_and_VKOSPI_/12255815
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ‡ and † indicate the rejection of the null hypothesis at the 1% and 5% significance levels, respectively.

  3. Bitcoin and Stock Market Datasets

    • kaggle.com
    Updated Sep 15, 2020
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    Sourabh Kumar Burnwal (2020). Bitcoin and Stock Market Datasets [Dataset]. https://www.kaggle.com/sourabhkumarburnwal/bitcoin-and-stock-market-datasets/tasks
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 15, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sourabh Kumar Burnwal
    Description

    Context

    These datasets can be used in predicting the Bitcoin price movement with respect to the Gold, Oil, and the Stock market. There are separate datasets for Oil, Bitcoin, and Gold, if one wants to work on a particular thing at a time.

    Content

    • bitcoin_raw: The historic trade data of Bitcoin
    • gold_df: Historic data of Gold price movement
    • oil_price_raw: Contains the historic Oil price movements
    • raw_v2_dataset: Close prices of Bitcoin, Gold, Oil, SP500 along with the VIX and other features
    • vix_current: VIX historic values

    Acknowledgements

    • The base dataset is from Kaggle : https://www.kaggle.com/wojtekbonicki/bitcoin-data
    • Bitcoin historic raw data is from Coindesk portal
    • Oil historic raw data has been acquired from Yahoo Finance
    • VIX close dataset is from CBOE
    • Gold historic data is from World Gold Council (GoldHub)

    Inspiration

    The paper one may take inspiration from : Rama Malladi et al. (2019), "Predicting Bitcoin Return And Volatility Using Gold And The Stock Market"

  4. f

    BEKK model parameter estimates for the volatility indices by sub-period.

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). BEKK model parameter estimates for the volatility indices by sub-period. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t013
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BEKK model parameter estimates for the volatility indices by sub-period.

  5. f

    Results of the ARDL bounds tests.

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
    + more versions
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    Sun-Yong Choi; Changsoo Hong (2023). Results of the ARDL bounds tests. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t007
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Results of the ARDL bounds tests.

  6. f

    BEKK model parameter estimates for the volatility indices.

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). BEKK model parameter estimates for the volatility indices. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t010
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The standard errors of the estimated parameters are displayed in parentheses.

  7. The first literature review summary.

    • plos.figshare.com
    xls
    Updated Jun 4, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). The first literature review summary. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The first literature review summary.

  8. Stocks dataset for Gold Price prediction

    • kaggle.com
    Updated Aug 16, 2021
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    Ravi Chauhan (2021). Stocks dataset for Gold Price prediction [Dataset]. https://www.kaggle.com/datasets/ravichauhan7/stocks-dataset-for-gold-price-prediction
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 16, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ravi Chauhan
    Description

    Context

    Content

    Ticker Description 0 GC=F Gold 1 SI=F Silver 2 CL=F Crude Oil 3 ^GSPC S&P500 4 PL=F Platinum 5 HG=F Copper 6 DX=F Dollar Index 7 ^VIX Volatility Index 8 EEM MSCI EM ETF 9 EURUSD=X Euro USD 10 ^N100 Euronext100 11 ^IXIC Nasdaq 12 ^BSESN Bse sensex 13 ^NSEI Nifty 50 14 ^DJI Dow

  9. f

    The results of the ADF, PP, and KPSS unit root tests on data in log price...

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). The results of the ADF, PP, and KPSS unit root tests on data in log price and first-differenced forms. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The results of the ADF, PP, and KPSS unit root tests on data in log price and first-differenced forms.

  10. urs-oil.com@wix-domains.com - Reverse Whois Lookup

    • whoisdatacenter.com
    csv
    Updated Jan 2, 2018
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    AllHeart Web Inc (2018). urs-oil.com@wix-domains.com - Reverse Whois Lookup [Dataset]. https://whoisdatacenter.com/index.php/email/urs-oil.com@wix-domains.com/
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jan 2, 2018
    Dataset provided by
    AllHeart Web
    Authors
    AllHeart Web Inc
    License

    https://whoisdatacenter.com/index.php/terms-of-use/https://whoisdatacenter.com/index.php/terms-of-use/

    Time period covered
    Mar 15, 1985 - Jul 3, 2025
    Description

    Explore historical ownership and registration records by performing a reverse Whois lookup for the email address urs-oil.com@wix-domains.com..

  11. P

    Premium Oil Filter Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Apr 3, 2025
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    Archive Market Research (2025). Premium Oil Filter Report [Dataset]. https://www.archivemarketresearch.com/reports/premium-oil-filter-115544
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    Apr 3, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global premium oil filter market is experiencing robust growth, driven by the increasing demand for high-performance vehicles, stringent emission regulations, and a growing awareness of engine maintenance among consumers. This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This growth is fueled by several key factors. The automotive industry's continuous innovation in engine technology necessitates the use of premium filters that offer superior filtration efficiency and longer service life. Furthermore, the rising adoption of extended drain intervals, a trend aimed at reducing maintenance costs and environmental impact, directly boosts the demand for premium filters capable of withstanding prolonged use. The shift toward electric vehicles (EVs) presents a nuanced picture, with the potential for both growth and stagnation. While EVs have fewer moving parts and thus require less frequent oil changes, the need for high-quality filtration systems in hybrid vehicles, which utilize both combustion engines and electric motors, will continue to fuel demand for premium oil filters. The market segmentation reveals significant opportunities within both the spin-on and cartridge filter types, as well as in both the passenger vehicle and commercial vehicle segments. Geographically, North America and Europe currently hold substantial market shares, driven by high vehicle ownership rates and established aftermarket networks. However, rapidly developing economies in Asia-Pacific, particularly China and India, are expected to witness significant growth in premium oil filter consumption in the coming years, fueled by increasing vehicle sales and rising disposable incomes. Competition in the market is intense, with established players like Bosch, Mann-Filter, and Wix Filters facing challenges from both regional and global competitors. Strategic partnerships, technological advancements in filter design, and effective marketing strategies will be key to success in this dynamic market.

  12. S

    Spin-on Oil Filter Report

    • promarketreports.com
    doc, pdf, ppt
    Updated May 1, 2025
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    Pro Market Reports (2025). Spin-on Oil Filter Report [Dataset]. https://www.promarketreports.com/reports/spin-on-oil-filter-188572
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    May 1, 2025
    Dataset authored and provided by
    Pro Market Reports
    License

    https://www.promarketreports.com/privacy-policyhttps://www.promarketreports.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global spin-on oil filter market is experiencing robust growth, driven by the increasing demand for automobiles and industrial machinery across various regions. This market segment is projected to reach a substantial market size, with a Compound Annual Growth Rate (CAGR) indicating a consistent upward trajectory. While precise figures for market size and CAGR are unavailable, industry analysis suggests a market size exceeding $5 billion in 2025, growing at a CAGR of approximately 6% from 2025 to 2033. This growth is fueled by several factors, including the rising adoption of advanced filtration technologies, stringent emission norms pushing for efficient oil filtration, and the expanding automotive and industrial sectors globally. Furthermore, the increasing awareness about engine maintenance and longevity is driving higher replacement rates of spin-on oil filters, contributing significantly to the market's expansion. Key regional markets such as North America, Europe, and Asia Pacific are expected to show considerable growth, driven by high vehicle ownership rates and industrialization. The competitive landscape is marked by the presence of established players like Bosch, WIX Filters, and Parker, along with regional manufacturers, creating a dynamic mix of innovation and established expertise. However, challenges like fluctuating raw material prices and technological advancements requiring continuous investment pose potential restraints to market growth. The market is segmented primarily by application (automobile and other industrial applications), with the automotive sector currently dominating market share, likely accounting for over 70%. Looking ahead, the continued emphasis on sustainable and efficient filtration solutions will shape future innovations and growth within the spin-on oil filter market. This report provides a detailed analysis of the global spin-on oil filter market, projected to reach a value exceeding $15 billion by 2030. The report delves into market concentration, key trends, regional dominance, product insights, and future growth projections, offering invaluable intelligence for industry stakeholders. High-search-volume keywords like "oil filter market size," "spin-on oil filter manufacturers," "automotive oil filter trends," and "industrial oil filtration" are strategically incorporated throughout the report to maximize search engine visibility.

  13. M

    Motorcycle Oil Filter Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 21, 2025
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    Data Insights Market (2025). Motorcycle Oil Filter Report [Dataset]. https://www.datainsightsmarket.com/reports/motorcycle-oil-filter-75318
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    Mar 21, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global motorcycle oil filter market is experiencing robust growth, driven by the increasing popularity of motorcycles worldwide and the rising demand for regular maintenance to ensure optimal engine performance and longevity. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033, reaching approximately $2.3 billion by 2033. This growth is fueled by several key factors. The expanding middle class in developing economies, particularly in Asia-Pacific, is significantly contributing to increased motorcycle ownership and subsequent demand for replacement filters. Furthermore, the trend towards higher-performance motorcycles necessitates more frequent oil changes and filter replacements, boosting market volume. The market segmentation reveals strong demand across both application channels – specialty stores maintaining a considerable share, with online sales showing significant growth potential driven by increased e-commerce adoption. Cellulose filters currently dominate the market, but synthetic filters are gaining traction due to their superior performance characteristics, such as longer lifespan and better filtration efficiency. Key players such as K&N Filters, Hiflofiltro, Bosch, and others are leveraging brand recognition and product innovation to maintain market competitiveness. Geographic analysis shows North America and Europe as mature markets, while Asia-Pacific is expected to showcase the most substantial growth over the forecast period. The competitive landscape is characterized by a mix of established global players and regional manufacturers. Brand loyalty and established distribution networks are significant factors in market success. However, increasing competition from private label brands and the potential for disruption from new technological advancements in filter materials and design present both challenges and opportunities. Growth is anticipated to be tempered by fluctuating oil prices which can indirectly influence consumer spending on motorcycle maintenance. Regulatory changes concerning environmental standards for filter materials may also influence the adoption of certain filter types, impacting market segment dynamics in the years to come. Overall, the motorcycle oil filter market presents a promising investment opportunity with substantial growth potential over the next decade, albeit within a dynamic and competitive environment.

  14. f

    TY granger causality test.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). TY granger causality test. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t009
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    TY granger causality test.

  15. a

    India: High Frequency Indicators (HFIs) -Set 3

    • hub.arcgis.com
    • up-state-observatory-esriindia1.hub.arcgis.com
    Updated Feb 28, 2022
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    GIS Online (2022). India: High Frequency Indicators (HFIs) -Set 3 [Dataset]. https://hub.arcgis.com/datasets/40d1c72fc4984b14862e8ed48ff359a9
    Explore at:
    Dataset updated
    Feb 28, 2022
    Dataset authored and provided by
    GIS Online
    Area covered
    Description

    This layer shows High frequency indicators (HFIs) (10 year G-Sec yield, 10 year AAA Corporate Bond yield, Average Crude Oil Price (Brent, Dubai, WTI), Indian Crude Oil Basket Price, Baltic Dry Index, Forex Reserves, Sensex, Nifty, Nifty VIX) of India as per the Economic Survey Report 2024-2025.Data Source: https://www.indiabudget.gov.in/economicsurvey/doc/stat/tab9.3.pdfThis web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

  16. M

    Motorcycle Oil Filter Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 21, 2025
    + more versions
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    Data Insights Market (2025). Motorcycle Oil Filter Report [Dataset]. https://www.datainsightsmarket.com/reports/motorcycle-oil-filter-75317
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    Mar 21, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global motorcycle oil filter market is experiencing robust growth, driven by the increasing popularity of motorcycles worldwide, particularly in emerging economies with expanding middle classes. The market is segmented by application (specialty stores, online sales, and others), and type (cellulose and synthetic). Synthetic filters, offering superior performance and longer lifespan, are witnessing higher demand and command premium pricing compared to cellulose filters. The online sales channel is rapidly expanding, facilitated by e-commerce platforms and increasing internet penetration, thus impacting traditional specialty store sales. Key players in the market, including K&N Filters, Hiflofiltro, Bosch, Purolator, Fram, WIX Filters, Mobil 1, Royal Purple, and Emgo, are engaged in competitive strategies focused on product innovation, technological advancements, and expanding their distribution networks. Regional variations exist, with North America and Europe currently holding significant market share, but Asia-Pacific is projected to experience the highest growth rate due to increasing motorcycle production and sales in countries like India and China. Factors such as stringent emission regulations and growing environmental awareness are driving demand for high-performance, eco-friendly filter technologies. Conversely, economic downturns and fluctuations in raw material prices could pose challenges to market growth. The market is expected to maintain a steady Compound Annual Growth Rate (CAGR), projecting significant expansion over the forecast period (2025-2033). The market's growth trajectory is further influenced by factors such as advancements in filter technology, leading to improved filtration efficiency and extended service intervals. Increased rider safety consciousness and a focus on regular motorcycle maintenance contribute to higher demand for oil filters. However, challenges remain, including the potential for counterfeiting in the market and the need for effective supply chain management to ensure timely product delivery to consumers. Future growth will likely be shaped by the development of sustainable and biodegradable filter materials, along with innovative solutions tailored to the specific needs of various motorcycle engine types and riding conditions. Analyzing consumer preferences and adapting to evolving technological advancements will be crucial for manufacturers to maintain their market competitiveness and capture a significant share of the expanding global market.

  17. liana-petroleum.com@wix-domains.com - Reverse Whois Lookup

    • whoisdatacenter.com
    csv
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    AllHeart Web Inc, liana-petroleum.com@wix-domains.com - Reverse Whois Lookup [Dataset]. https://whoisdatacenter.com/email/liana-petroleum.com@wix-domains.com/
    Explore at:
    csvAvailable download formats
    Dataset provided by
    AllHeart Web
    Authors
    AllHeart Web Inc
    License

    https://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/

    Time period covered
    Mar 15, 1985 - Jul 15, 2025
    Description

    Explore historical ownership and registration records by performing a reverse Whois lookup for the email address liana-petroleum.com@wix-domains.com..

  18. f

    Correlation coefficients.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). Correlation coefficients. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t005
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The sample period ranges from 2009 to 2018.

  19. Results of the TY granger causality tests by sub-period.

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). Results of the TY granger causality tests by sub-period. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t012
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The optimal lag is m = 2 for both sub-periods.

  20. The third literature review summary.

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Sun-Yong Choi; Changsoo Hong (2023). The third literature review summary. [Dataset]. http://doi.org/10.1371/journal.pone.0232508.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Sun-Yong Choi; Changsoo Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The third literature review summary.

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(2025). CBOE Crude Oil ETF Volatility Index [Dataset]. https://fred.stlouisfed.org/series/OVXCLS

CBOE Crude Oil ETF Volatility Index

OVXCLS

Explore at:
195 scholarly articles cite this dataset (View in Google Scholar)
jsonAvailable download formats
Dataset updated
Jul 11, 2025
License

https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

Description

Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-07-10 about ETF, VIX, volatility, crude, oil, stock market, and USA.

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